Paper ID: 2210.12160

On the connection between Bregman divergence and value in regularized Markov decision processes

Brendan O'Donoghue

In this short note we derive a relationship between the Bregman divergence from the current policy to the optimal policy and the suboptimality of the current value function in a regularized Markov decision process. This result has implications for multi-task reinforcement learning, offline reinforcement learning, and regret analysis under function approximation, among others.

Submitted: Oct 21, 2022